It is well-known from the literature that the Fisher information matrix gives statistical evidence about the information content of the underlying data and the co-variance of the estimated parameters [1]}, thus it can be seen as a reliable measure for parameter uncertainty: high variances usually point to high parameter uncertainties. Deduced from the global Fisher information matrix [1]}, the local Fisher information matrix for the \(i\) th rule is defined by:
\(F_i = \sum _{k=1}^N \Psi _i(k) \frac{\partial \hat{y}(\mathbf {x}_k)}{\partial \Phi } \frac{\partial \hat{y}(\mathbf {x}_k)}{\partial \Phi }^T\) 
